Keras Tuner를 사용하여 하이퍼파라미터 찾는 예제

Keras Tuner를 사용하여 Keras 모델의 레이어, 학습률, 배치 크기 등에 대한 최적의 하이퍼파라미터를 찾는 예제 코드입니다. 

 

 

2022. 02. 20  최초작성

아래 링크들을 참고하여 Red Wine Quality 데이터셋(https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009)을 위해 작성한 Keras classification 코드를 최적하는 코드를 작성해보았습니다. 

Model을 구성하는 Dense 레이어의 units, Dropou의 rate, 학습률, 배치 크기 등의 하이퍼파라미터에 대한 최적값을 찾게됩니다. 

 

https://www.tensorflow.org/tutorials/keras/keras_tuner?hl=ko 

 

https://www.sicara.ai/blog/hyperparameter-tuning-keras-tuner 

 

https://github.com/keras-team/keras-tuner/issues/122 

실행 결과 로그와 그래프입니다. 원하는 수준의 결과를 얻을 수는 없었지만 Keras Tuner를 사용 방법을 간단하게나마 알게된 소득이 있었습니다.

자주 사용해보며 Keras Tuner를 좀 더 익혀봐야 겠습니다.

 

 

로그는 Keras Tuner 마지막 단계에서 실행된 최종 학습시 결과입니다.

 

최적 파라미터 값들

units_1 : 29 

units_2 : 107 

dropout_1 : 0.30000000000000004 

dropout_2 : 0.25 

learning_rate : 0.0005550246291195939 

batch_size : 32 

학습 로그

Epoch 1/100

2022-02-19 22:43:26.528003: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

32/32 [==============================] - ETA: 0s - loss: 2.7863 - accuracy: 0.14102022-02-19 22:43:31.225551: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

32/32 [==============================] - 6s 125ms/step - loss: 2.7863 - accuracy: 0.1410 - val_loss: 2.0815 - val_accuracy: 0.4554

Epoch 2/100

32/32 [==============================] - 2s 54ms/step - loss: 2.2213 - accuracy: 0.2810 - val_loss: 1.9345 - val_accuracy: 0.4554

Epoch 3/100

32/32 [==============================] - 1s 38ms/step - loss: 1.9900 - accuracy: 0.3793 - val_loss: 1.7907 - val_accuracy: 0.4821

Epoch 4/100

32/32 [==============================] - 1s 40ms/step - loss: 1.8068 - accuracy: 0.4767 - val_loss: 1.6511 - val_accuracy: 0.4821

Epoch 5/100

32/32 [==============================] - 1s 41ms/step - loss: 1.7116 - accuracy: 0.4777 - val_loss: 1.5355 - val_accuracy: 0.5268

Epoch 6/100

32/32 [==============================] - 1s 37ms/step - loss: 1.6246 - accuracy: 0.4846 - val_loss: 1.4224 - val_accuracy: 0.5625

Epoch 7/100

32/32 [==============================] - 1s 37ms/step - loss: 1.5000 - accuracy: 0.5253 - val_loss: 1.3281 - val_accuracy: 0.5625

Epoch 8/100

32/32 [==============================] - 1s 36ms/step - loss: 1.4924 - accuracy: 0.5362 - val_loss: 1.2561 - val_accuracy: 0.5804

Epoch 9/100

32/32 [==============================] - 1s 36ms/step - loss: 1.3701 - accuracy: 0.5641 - val_loss: 1.1988 - val_accuracy: 0.5714

Epoch 10/100

32/32 [==============================] - 1s 37ms/step - loss: 1.3380 - accuracy: 0.5561 - val_loss: 1.1497 - val_accuracy: 0.5714

Epoch 11/100

32/32 [==============================] - 1s 36ms/step - loss: 1.2951 - accuracy: 0.5541 - val_loss: 1.1113 - val_accuracy: 0.5714

Epoch 12/100

32/32 [==============================] - 1s 36ms/step - loss: 1.2427 - accuracy: 0.5412 - val_loss: 1.0836 - val_accuracy: 0.5804

Epoch 13/100

32/32 [==============================] - 1s 38ms/step - loss: 1.2034 - accuracy: 0.5581 - val_loss: 1.0662 - val_accuracy: 0.5625

Epoch 14/100

32/32 [==============================] - 1s 37ms/step - loss: 1.1908 - accuracy: 0.5740 - val_loss: 1.0530 - val_accuracy: 0.5714

Epoch 15/100

32/32 [==============================] - 1s 36ms/step - loss: 1.1685 - accuracy: 0.5561 - val_loss: 1.0403 - val_accuracy: 0.5804

Epoch 16/100

32/32 [==============================] - 1s 36ms/step - loss: 1.1344 - accuracy: 0.5641 - val_loss: 1.0497 - val_accuracy: 0.5714

Epoch 17/100

32/32 [==============================] - 1s 37ms/step - loss: 1.1212 - accuracy: 0.5899 - val_loss: 1.0407 - val_accuracy: 0.5625

Epoch 18/100

32/32 [==============================] - 1s 37ms/step - loss: 1.1054 - accuracy: 0.5730 - val_loss: 1.0418 - val_accuracy: 0.5714

Epoch 19/100

32/32 [==============================] - 1s 36ms/step - loss: 1.0839 - accuracy: 0.5809 - val_loss: 1.0195 - val_accuracy: 0.5804

Epoch 20/100

32/32 [==============================] - 1s 36ms/step - loss: 1.0589 - accuracy: 0.5889 - val_loss: 1.0247 - val_accuracy: 0.5536

Epoch 21/100

32/32 [==============================] - 1s 36ms/step - loss: 1.0402 - accuracy: 0.5809 - val_loss: 1.0233 - val_accuracy: 0.5625

Epoch 22/100

32/32 [==============================] - 1s 35ms/step - loss: 1.0504 - accuracy: 0.5740 - val_loss: 1.0120 - val_accuracy: 0.5625

Epoch 23/100

32/32 [==============================] - 1s 36ms/step - loss: 1.0328 - accuracy: 0.5631 - val_loss: 1.0067 - val_accuracy: 0.5625

Epoch 24/100

32/32 [==============================] - 1s 35ms/step - loss: 1.0515 - accuracy: 0.5760 - val_loss: 0.9912 - val_accuracy: 0.5804

Epoch 25/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9994 - accuracy: 0.5750 - val_loss: 0.9904 - val_accuracy: 0.5893

Epoch 26/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9845 - accuracy: 0.6008 - val_loss: 0.9928 - val_accuracy: 0.5893

Epoch 27/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9838 - accuracy: 0.5780 - val_loss: 0.9888 - val_accuracy: 0.5982

Epoch 28/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9831 - accuracy: 0.5859 - val_loss: 0.9912 - val_accuracy: 0.6071

Epoch 29/100

32/32 [==============================] - 1s 36ms/step - loss: 1.0010 - accuracy: 0.5789 - val_loss: 0.9827 - val_accuracy: 0.5982

Epoch 30/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9849 - accuracy: 0.5899 - val_loss: 0.9795 - val_accuracy: 0.5804

Epoch 31/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9661 - accuracy: 0.5938 - val_loss: 0.9953 - val_accuracy: 0.5714

Epoch 32/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9692 - accuracy: 0.6077 - val_loss: 0.9831 - val_accuracy: 0.5804

Epoch 33/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9740 - accuracy: 0.5789 - val_loss: 0.9850 - val_accuracy: 0.5982

Epoch 34/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9564 - accuracy: 0.5849 - val_loss: 0.9904 - val_accuracy: 0.5804

Epoch 35/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9842 - accuracy: 0.5819 - val_loss: 0.9747 - val_accuracy: 0.5804

Epoch 36/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9768 - accuracy: 0.5809 - val_loss: 0.9648 - val_accuracy: 0.6071

Epoch 37/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9520 - accuracy: 0.5938 - val_loss: 0.9449 - val_accuracy: 0.5982

Epoch 38/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9181 - accuracy: 0.6018 - val_loss: 0.9482 - val_accuracy: 0.5893

Epoch 39/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9461 - accuracy: 0.5998 - val_loss: 0.9420 - val_accuracy: 0.6071

Epoch 40/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9496 - accuracy: 0.5929 - val_loss: 0.9542 - val_accuracy: 0.6161

Epoch 41/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9516 - accuracy: 0.5948 - val_loss: 0.9650 - val_accuracy: 0.6071

Epoch 42/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9423 - accuracy: 0.6018 - val_loss: 0.9572 - val_accuracy: 0.5982

Epoch 43/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9152 - accuracy: 0.6107 - val_loss: 0.9574 - val_accuracy: 0.5893

Epoch 44/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9391 - accuracy: 0.5899 - val_loss: 0.9592 - val_accuracy: 0.5982

Epoch 45/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9295 - accuracy: 0.6058 - val_loss: 0.9574 - val_accuracy: 0.5982

Epoch 46/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9276 - accuracy: 0.6226 - val_loss: 0.9530 - val_accuracy: 0.6071

Epoch 47/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9365 - accuracy: 0.5998 - val_loss: 0.9548 - val_accuracy: 0.6071

Epoch 48/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9343 - accuracy: 0.6097 - val_loss: 0.9502 - val_accuracy: 0.6161

Epoch 49/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9080 - accuracy: 0.6286 - val_loss: 0.9561 - val_accuracy: 0.5982

Epoch 50/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9198 - accuracy: 0.6087 - val_loss: 0.9621 - val_accuracy: 0.5982

Epoch 51/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9143 - accuracy: 0.6028 - val_loss: 0.9590 - val_accuracy: 0.6071

Epoch 52/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9194 - accuracy: 0.6008 - val_loss: 0.9491 - val_accuracy: 0.6161

Epoch 53/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8980 - accuracy: 0.6207 - val_loss: 0.9603 - val_accuracy: 0.5982

Epoch 54/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9141 - accuracy: 0.6157 - val_loss: 0.9443 - val_accuracy: 0.5893

Epoch 55/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9140 - accuracy: 0.5869 - val_loss: 0.9466 - val_accuracy: 0.6071

Epoch 56/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9131 - accuracy: 0.6028 - val_loss: 0.9506 - val_accuracy: 0.5982

Epoch 57/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9075 - accuracy: 0.6197 - val_loss: 0.9699 - val_accuracy: 0.5893

Epoch 58/100

32/32 [==============================] - 1s 36ms/step - loss: 0.8932 - accuracy: 0.6266 - val_loss: 0.9607 - val_accuracy: 0.6071

Epoch 59/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8836 - accuracy: 0.6177 - val_loss: 0.9422 - val_accuracy: 0.6071

Epoch 60/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8945 - accuracy: 0.5948 - val_loss: 0.9509 - val_accuracy: 0.6161

Epoch 61/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8984 - accuracy: 0.6137 - val_loss: 0.9500 - val_accuracy: 0.5982

Epoch 62/100

32/32 [==============================] - 1s 36ms/step - loss: 0.9146 - accuracy: 0.5909 - val_loss: 0.9549 - val_accuracy: 0.5982

Epoch 63/100

32/32 [==============================] - 1s 36ms/step - loss: 0.8777 - accuracy: 0.6316 - val_loss: 0.9597 - val_accuracy: 0.6071

Epoch 64/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9064 - accuracy: 0.6147 - val_loss: 0.9508 - val_accuracy: 0.6161

Epoch 65/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9050 - accuracy: 0.6187 - val_loss: 0.9531 - val_accuracy: 0.6161

Epoch 66/100

32/32 [==============================] - 1s 36ms/step - loss: 0.8967 - accuracy: 0.6216 - val_loss: 0.9530 - val_accuracy: 0.6161

Epoch 67/100

32/32 [==============================] - 1s 35ms/step - loss: 0.9176 - accuracy: 0.6107 - val_loss: 0.9472 - val_accuracy: 0.5893

Epoch 68/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8828 - accuracy: 0.6246 - val_loss: 0.9480 - val_accuracy: 0.5893

Epoch 69/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8782 - accuracy: 0.6167 - val_loss: 0.9490 - val_accuracy: 0.5893

Epoch 70/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8861 - accuracy: 0.6087 - val_loss: 0.9530 - val_accuracy: 0.5804

Epoch 71/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8846 - accuracy: 0.6068 - val_loss: 0.9401 - val_accuracy: 0.6071

Epoch 72/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8866 - accuracy: 0.6266 - val_loss: 0.9293 - val_accuracy: 0.6071

Epoch 73/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8646 - accuracy: 0.6286 - val_loss: 0.9485 - val_accuracy: 0.6161

Epoch 74/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8859 - accuracy: 0.6137 - val_loss: 0.9561 - val_accuracy: 0.6161

Epoch 75/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8670 - accuracy: 0.6197 - val_loss: 0.9547 - val_accuracy: 0.5982

Epoch 76/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8983 - accuracy: 0.6266 - val_loss: 0.9488 - val_accuracy: 0.5982

Epoch 77/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8869 - accuracy: 0.6197 - val_loss: 0.9448 - val_accuracy: 0.5982

Epoch 78/100

32/32 [==============================] - 1s 36ms/step - loss: 0.8709 - accuracy: 0.6296 - val_loss: 0.9419 - val_accuracy: 0.6071

Epoch 79/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8584 - accuracy: 0.6475 - val_loss: 0.9415 - val_accuracy: 0.6071

Epoch 80/100

32/32 [==============================] - 1s 36ms/step - loss: 0.8674 - accuracy: 0.6445 - val_loss: 0.9386 - val_accuracy: 0.6071

Epoch 81/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8648 - accuracy: 0.6147 - val_loss: 0.9407 - val_accuracy: 0.6161

Epoch 82/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8997 - accuracy: 0.6197 - val_loss: 0.9491 - val_accuracy: 0.6250

Epoch 83/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8890 - accuracy: 0.6137 - val_loss: 0.9408 - val_accuracy: 0.6071

Epoch 84/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8603 - accuracy: 0.6236 - val_loss: 0.9302 - val_accuracy: 0.6339

Epoch 85/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8646 - accuracy: 0.6266 - val_loss: 0.9253 - val_accuracy: 0.6339

Epoch 86/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8550 - accuracy: 0.6395 - val_loss: 0.9263 - val_accuracy: 0.5982

Epoch 87/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8652 - accuracy: 0.6346 - val_loss: 0.9340 - val_accuracy: 0.5982

Epoch 88/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8613 - accuracy: 0.6356 - val_loss: 0.9337 - val_accuracy: 0.6250

Epoch 89/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8473 - accuracy: 0.6415 - val_loss: 0.9300 - val_accuracy: 0.5982

Epoch 90/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8607 - accuracy: 0.6395 - val_loss: 0.9403 - val_accuracy: 0.6071

Epoch 91/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8605 - accuracy: 0.6365 - val_loss: 0.9359 - val_accuracy: 0.6339

Epoch 92/100

32/32 [==============================] - 1s 36ms/step - loss: 0.8550 - accuracy: 0.6236 - val_loss: 0.9489 - val_accuracy: 0.6429

Epoch 93/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8666 - accuracy: 0.6356 - val_loss: 0.9502 - val_accuracy: 0.6071

Epoch 94/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8636 - accuracy: 0.6246 - val_loss: 0.9619 - val_accuracy: 0.6161

Epoch 95/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8706 - accuracy: 0.6147 - val_loss: 0.9575 - val_accuracy: 0.6161

Epoch 96/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8507 - accuracy: 0.6276 - val_loss: 0.9539 - val_accuracy: 0.5982

Epoch 97/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8310 - accuracy: 0.6356 - val_loss: 0.9621 - val_accuracy: 0.6161

Epoch 98/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8476 - accuracy: 0.6465 - val_loss: 0.9518 - val_accuracy: 0.6339

Epoch 99/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8512 - accuracy: 0.6375 - val_loss: 0.9571 - val_accuracy: 0.6250

Epoch 100/100

32/32 [==============================] - 1s 35ms/step - loss: 0.8517 - accuracy: 0.6316 - val_loss: 0.9530 - val_accuracy: 0.6429

2022-02-19 22:46:01.959095: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

 

테스트 데이터셋에 대한 추론  정확도

0.58125

전체 코드입니다.

# 참고 # # https://www.sicara.ai/blog/hyperparameter-tuning-keras-tuner # https://www.tensorflow.org/tutorials/keras/keras_tuner?hl=ko # https://github.com/keras-team/keras-tuner/issues/122 from tensorflow import keras from tensorflow.keras import layers, optimizers from tensorflow.keras.utils import to_categorical from sklearn.metrics import accuracy_score import numpy as np import pandas as pd import matplotlib.pyplot as plt import os import kerastuner as kt # 하이퍼파리미터를 찾기 위해 모델에서 조정할 레이어의 값 범위를 지정해주고 추가로 학습률과 배치 크기의 값 범위를 추가로 지정해줍니다. class MyHyperModel(kt.HyperModel):   def build(self, hp):     hp_units1 = hp.Int('units_1', min_value = 16, max_value = 180)     hp_units2 = hp.Int('units_2', min_value = 16, max_value = 180)     hp_units4 = hp.Float('dropout_1', min_value=0.0, max_value=0.5, step=0.05)     hp_units5 = hp.Float('dropout_2', min_value=0.0, max_value=0.5, step=0.05)     model = keras.Sequential()     model.add(layers.Dense(hp_units1, activation='relu', input_shape=[X_train.shape[1]]))     model.add(layers.BatchNormalization())     model.add(layers.Dropout(hp_units4))     model.add(layers.Dense(hp_units2, activation='relu'))     model.add(layers.BatchNormalization())     model.add(layers.Dropout(hp_units5))     model.add(layers.Dense(9, activation='softmax'))     hp_learning_rate = hp.Float('learning_rate', min_value=1e-6, max_value=1e-3, sampling='LOG')     model.compile(optimizer=optimizers.Adam(learning_rate=hp_learning_rate), loss='categorical_crossentropy', metrics=['accuracy'])     return model   def fit(self, hp, model, *args, **kwargs):       return model.fit(           *args,           batch_size=hp.Int('batch_size', min_value = 16, max_value = 256, step = 16),           **kwargs,       ) # Red Wine Quality 데이터셋을 로드합니다. BASE_DIR = os.path.dirname(os.path.abspath(__file__)) red_wine = pd.read_csv(BASE_DIR + '/' + 'winequality-red.csv') red_wine = red_wine.dropna() # 7:3 비율로 training dataset과 Test dataset으로 분리합니다. df_train = red_wine.sample(frac=0.7, random_state=1234) df_test = red_wine.drop(df_train.index) # 특징과 라벨을 분리합니다. X_train = df_train.drop('quality', axis=1).values X_test = df_test.drop('quality', axis=1).values y_train = df_train['quality'].values y_test = df_test['quality'].values # min-max normalization를 적용하여 특징 값의 범위를 0 ~ 1 사이로 변경합니다. # 신경망은 입력이 공통 크기일 때 가장 잘 수행되는 경향이 있기 때문입니다. # 이때 특징별로 min,max를 구하여 min-max normalization를 적용합니다. X_train_max = X_train.max(axis=0) X_train_min = X_train.min(axis=0) X_train = (X_train - X_train_min) / (X_train_max - X_train_min) X_test = (X_test - X_train_min) / (X_train_max - X_train_min) # One-hot 인코딩을 합니다. y_train, y_test = to_categorical(y_train), to_categorical(y_test) # 최적 하이퍼파라미터를 찾는 작업을 진행합니다. tuner = kt.Hyperband(MyHyperModel(),                     objective = 'val_accuracy',                     max_epochs = 100,                     executions_per_trial = 3,                     overwrite = True,                     factor = 3) tuner.search(X_train, y_train, epochs = 100, validation_split = 0.1) # 최적 하이퍼파라미터를 가져옵니다. best_hps = tuner.get_best_hyperparameters()[0] # 최적 하이퍼파라미터를 출력합니다. print(f""" units_1 : {best_hps.get('units_1')} units_2 : {best_hps.get('units_2')} dropout_1 : {best_hps.get('dropout_1')} dropout_2 : {best_hps.get('dropout_2')} learning_rate : {best_hps.get('learning_rate')} batch_size : {best_hps.get('batch_size')} """) # 배치 크기는 따로 저장했다가 fit 메소드에서 적용합니다. batch_size = best_hps.get('batch_size') # 최적값으로 모델을 생성합니다. model = tuner.hypermodel.build(best_hps) # 학습을 진행합니다. # validation_split 아규먼트로 Training 데이터셋의 10%를 Validation 데이터셋으로 사용하도록합니다. # 예를 들어 배치 크기가 256이라는 것은 전체 데이터셋을 샘플 256개씩으로 나누어서 학습에 사용한다는 의미입니다. # 예를 들어 에포크(epochs)가 10이라는 것은 전체 train 데이터셋을 10번 본다는 의미입니다. history = model.fit(   X_train, y_train,   validation_split = 0.1,   batch_size=batch_size,   epochs=100, ) # 학습중 손실 변화를 그래프로 그립니다. history_df = pd.DataFrame(history.history) history_df['loss'].plot() history_df['val_loss'].plot() plt.legend() plt.show() history_df = pd.DataFrame(history.history) history_df['accuracy'].plot() history_df['val_accuracy'].plot() plt.legend() plt.show() # Test 데이터셋으로 모델 성능 평가를 합니다. y_pred = model.predict(X_test) y_pred = np.argmax(y_pred, axis=1) y_test = np.argmax(y_test, axis=1) print(accuracy_score(y_test, y_pred))